With the increase of location-based services, Web contents are being geo-tagged, and spatial keyword queries that retrieve objects satisfying both spatial and keyword conditions are gaining in prevalence. Unfortunatel...
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OONS is a new Object Oriented Neural Simulator. The goal creating it is making the construction of neural model as quickly and easily as possible for the users, and can run in shorter time than other simulators. OONS ...
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SUMMARY In this paper, we propose three Deflection-Routing-based Multicast (DRM) schemes for a bufferless NoC. The DRM scheme without packets replication (DRM-noPR) sends multicast packet through a non deterministic p...
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Cyber-Physical Systems (CPS) involve deep interactions between computation cores, communication networks, and physical environments. These systems are inherently complex and highly nondeterministic. This makes the tra...
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ISBN:
(纸本)9781457721205
Cyber-Physical Systems (CPS) involve deep interactions between computation cores, communication networks, and physical environments. These systems are inherently complex and highly nondeterministic. This makes the traditional formal verification technology impractical to verify the complete system behavior, and testing alone is insufficient to guarantee correctness. Runtime monitoring, known as a lightweight verification technique, provides a practical way to monitor and verify such systems at runtime. In this paper, we present a case study for runtime monitoring of the Cooperative Adaptive Cruise Control systems (CACC) in automobile CPS systems. We build a hybrid automatonbased model for the CACC system using the CHARON modeling language and construct an event-based runtime monitoring framework. The synthesized monitor observes the running of CACC and checks whether it works correctly against the temporal logic safety specification. Experimental results obtained through this case study provide evidence for the efficacy of runtime monitoring of CPS systems.
Gene expression microarray enables us to measure the gene expression levels for thousands of genes at the same time. Here, we constructed the non-negative matrix factorization analysis strategy (NMFAS) to dig the unde...
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Investor is a novel speculation scheme that targets at the open Internet. It makes use of standard HTTP and speculation by convention to fit into this environment. Investor is a runtime technique that doesn't modi...
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Investor is a novel speculation scheme that targets at the open Internet. It makes use of standard HTTP and speculation by convention to fit into this environment. Investor is a runtime technique that doesn't modify the languages, compilers or binary formats of the applications, so it is compatible well with existing programs or libraries. Investor can overcome some form of control dependency and data dependency. Preliminary experiments show that Investor can significantly improve the overall performance of the applications.
As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, it becomes an urgent problem of real-time energy-efficient scheduling in multiprocessor systems to reduce system energy cons...
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This work presents scalable algorithms for basic construction of parallel Radial Basis Probabilistic Neural Networks. The final goal is to build a neural network that can efficiently be implemented in distributed memo...
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ISBN:
(纸本)9781479902279
This work presents scalable algorithms for basic construction of parallel Radial Basis Probabilistic Neural Networks. The final goal is to build a neural network that can efficiently be implemented in distributed memory machines. Thus a fast simple parallel training scheme for RBPNNs is studied, that is based almost solely on Gaussian summations which can by their part be efficiently mapped on parallel as well as on pipeline distributed machines. The suggested training scheme is tested for accuracy and performance and can guarantee simplicity, parallelization and linear speed ups in common parallel implementations, namely neuron parallel and pipelining studied here.
Network virtualization is recognized as an effective way to overcome the ossification of the Internet. However, the virtual network mapping problem (VNMP) is a critical challenge, focusing on how to map the virtual ne...
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Network virtualization is recognized as an effective way to overcome the ossification of the Internet. However, the virtual network mapping problem (VNMP) is a critical challenge, focusing on how to map the virtual networks to the substrate network with efficient utilization of infrastructure resources. The problem can be divided into two phases: node mapping phase and link mapping phase. In the node mapping phase, the existing algorithms usually map those virtual nodes with a complete greedy strategy, without considering the topology among these virtual nodes, resulting in too long substrate paths (with multiple hops). Addressing this problem, we propose a topology awareness mapping algorithm, which considers the topology among these virtual nodes. In the link mapping phase, the new algorithm adopts the k-shortest path algorithm. Simulation results show that the new algorithm greatly increases the long-term average revenue, the acceptance ratio, and the long-term revenue-to-cost ratio (R/C).
The connotation of the cloud resources have been extended to be multi-scale resources, which includes central resources as presented by data center, edge resources as presented by Content Delivery Network (CDN) and en...
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The connotation of the cloud resources have been extended to be multi-scale resources, which includes central resources as presented by data center, edge resources as presented by Content Delivery Network (CDN) and end resources as presented by Peer-to-Peer (P2P). Under the development situation of the scale of the cloud services, it is difficult to provide services (e.g. streaming distribution) with guaranteed QoS only relying on single type of resource (e.g. central resources) to geo-distributed users. Therefore, making multi-resources cooperative to provide reliable services is necessary. However, it is a great challenge to realize Federated Management of Multi-scale Resources (FMMR). In this research, we propose the idea of prediction-based FMMR, and present the problem formulation introducing economic profit from the perspective of CDN operators. Then, we present the method of Time-series Prediction based on Wavelet Analysis (TPWA) to predict the resource requirements of streaming cloud services in CDN. Finally, the predictability of the resource requirement pattern of the streaming cloud service and the effectiveness of our proposed method have been verified, based on the traces collected from a real CDN entity.
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